ABSTRACT The U.S. general population suicide rate has increased steadily over the past 20 years. Those who have served in the U.S. Armed Forces are a high risk subgroup among which rates have increased at a faster rate as compared to those who have never served in the military. Emerging research suggests the existence of several subtypes of suicidal states. Individuals in different subtypes may follow different pathways to high risk states and may respond to treatment interventions in different ways. To date, studies have not examined typologies using integrated datasets that include genetic, environmental, medical, and psychological variables. To address this knowledge gap, we propose to leverage an archived dataset from the South Texas Region Organization Network Guiding Studies of Trauma and Resilience (STRONG STAR) Repository, which contains genetic, environmental, medical, and psychological variables from over 4000 military personnel who were assessed before and after deployment. Using this dataset, we will (a) identify subgroups of suicidal military personnel and (b) identify different patterns of increasing, decreasing, and static suicide risk. Results of this analysis will enable us to identify discrete genotype-phenotype expressions of suicide risk, thereby enabling us to identify multiple risk models that can be used to improve risk detection and refine suicide prevention interventions. Emerging research further indicates the process of suicide risk over time is nonlinear in nature. Unfortunately, the majority of studies examining the emergence of suicide risk over time have employed research and data analytic methods that are unable to accurately capture nonlinear change processes. To address this knowledge gap, we propose to leverage archived datasets from six clinical trials included in the STRONG STAR Repository (N>800), each of which includes repeated assessments (up to 13 total) of depression, PTSD, and suicide ideation. Multivariate latent change score models, informed by dynamical systems theory, will be used to model nonlinear change processes associated with low risk and high risk states. Results of this analysis will yield posterior probabilities that can estimate the likelihood of a given patient transitioning to a high risk state at a given point in time, which could lead to the development of “warning systems” that identify who will experience increased risk over time, and when. Although the proposed study uses archived data collected from military personnel, the proposed methods can be translated to other populations and settings, thereby leading to significant advances in the detection and individuals with elevated risk for suicide.